Child Neuropsychology Vol. 3(1):13-27 (1997) ISSN: (print 0929-7049)(online 1744-4136) doi:10.1080/09297049708401365

Child Neuropsychology Vol. 3(1):13-27 (1997)
ISSN: (print 0929-7049)(online 1744-4136)
doi:10.1080/09297049708401365
This is a peer reviewed pre-print version of the following article from Child Neuropsychology: Attention
Performance in an Epidemiological Sample of Urban Children: The Role of Gender and Verbal Intelligence, which
has been pulished in final form at:
http://www.psypress.com
http://www.tandf.co.uk/journals/titles/09297049.asp
http://www.tandfonline.com/doi/abs/10.1080/09297049708401365
© 1997 Taylor & Francis
Attention Performance in an Epidemiological Sample of Urban Children: The
Role of Gender and Verbal Intelligence
Daisy M. Pascualvaca, Section on Clinical and Experimental Neuropsychology,
Laboratory of Psychology and Psychopathology, National Institute of Mental
Health, Bruno J. Anthony, Section on Clinical and Experimental Neuropsychology,
Laboratory of Psychology and Psychopathology, National Institute of Mental
Health, and the Department of Psychiatry, University of Maryland School of
Medicine, L. Eugene Arnold, Child and Adolescent Disorders Research Branch,
National Institute of Mental Health, George W. Rebok, Prevention Research Center,
Department of Mental Hygiene, School of Hygiene and Public Health, Johns
Hopkins University, Mary Beth Ahearn, Prevention Research Center, Department
of Mental Hygiene, School of Hygiene and Public Health, Johns Hopkins University,
Sheppard G. Kellam, Prevention Research Center, Department of Mental Hygiene,
School of Hygiene and Public Health, Johns Hopkins University, Allan F. Mirsky,
Section on Clinical and Experimental Neuropsychology, Laboratory of Psychology
and Psychopathology, National Institute of Mental Health
Abstract
We administered a comprehensive attentional battery to an epidemiologically defined sample of 435 firstand second-grade children to assess the influence of gender and verbal intelligence on attention. The
battery included three versions of the continuous performance test (CPT), two digit cancellation tasks, three
subtests from the WISC-R, and the Wisconsin Card Sorting Test. The results indicated that both gender and
intelligence had an impact on attentional performance. Girls performed better than boys; they made fewer
errors on the CPT and obtained higher scores on the digit cancellation task and the Coding subtest of the
WISC-R. Children with higher verbal intelligence also performed better on the attentional tests, but this
advantage was not observed across measures or levels of performance. For example, children with limited
verbal skills performed significantly worse than their peers only in measures with high processing demands
(the degraded CPT and the distraction version of the digit cancellation task).
Problems with attention represent one of the most pervasive behavioral disturbances
encountered in psychiatric and educational contexts, and are estimated to affect as many as 30%
of all school-aged children (Cantwell, 1975; Kellam, Branch, Agrawal, & Ensminger 1975;
Offord et al., 1987; Rutter, Tizard, & Whitmore, 1970). Despite the ubiquity of attention deficits
and their detrimental effects on the child's functioning, little is known about the factors that
influence attentional performance in normal children. There is little information, for example,
regarding the impact of certain characteristics such as age, intelligence, and gender, or
environmental factors such as family background and socioeconomic status, on attentional
performance. In this study, we report on the role of two of these characteristics, intelligence and
gender; the impact of other factors such as ethnic background, socioeconomic status, and family
composition will be presented in a separate communication.
There is considerable evidence to indicate that age influences attentional performance.
For example, the capacity to sustain attention, inhibit inappropriate responses, and shift
attentional set all improve during childhood (Chelune & Baer, 1986; Gale & Lynn, 1972;
Greenberg & Waldman, 1993; Halperin, Sharma, Greenblatt, & Schwartz, 1991; Levy, 1980).
The importance of other characteristics such as intelligence and gender is less clear. Several
studies have found no association between attention and intelligence in children with intact
cognitive capacities (Gale & Lynn, 1972; Halperin et al., 1991). However, children with mental
retardation and borderline intelligence have more difficulty than their peers in sustaining a focus
of attention (Carter & Swanson, 1995; see Warm & Berch, 1985 for a review) and ignoring
distracting information (Hagen & Huntsman, 1971). There are several possibilities for the
discrepancy in these findings. It is possible, for example, that the relationship between attention
and intelligence is not uniform across the range of abilities or varies according to the specific
attention process. It is also possible that specific conditions or task requirements have a different
impact at various levels of intelligence. Unfortunately, these issues cannot be addressed with the
available data because most studies have either used a single objective measure of attention or
have only studied children within a narrow range of cognitive abilities.
The same limitations apply to the investigation of gender differences in attentional
capacities. Most studies of attentional performance in children have not focused on gender
differences, and the few studies that are available have used only one measure. Some studies
have found no differences between boys and girls in attention tests such as the Attentional
Capacity Test (Weber & Segalowitz, 1990) whereas studies using the Continuous Performance
Test have yielded inconsistent results (Greenberg & Waldman, 1993; Kirchner & Knopf, 1974;
Levy & Hobbes, 1979). Since these tests measure specific components of attention, the results
cannot be generalized to other attention processes. Furthermore, evidence from various lines of
investigation suggests that boys and girls may differ in their attention functions. For example,
many disorders that are characterized by attention problems, such as
Attention-Deficit/Hyperactivity Disorder (AD/HD), are not only more commonly diagnosed
among boys (American Psychiatric Association, 1994), but are expressed differently between the
genders (Berry, Shaywitz, & Shaywitz, 1985; Brown, Madan-Swain, & Baldwin, 1991; James &
Taylor, 1990). In addition, the symptoms associated with impaired attention appear to vary
depending on the child's gender. Studies of nonclinical samples report that attention problems
are associated with behavioral difficulties (Halperin et al., 1988; Kellam et al., 1975; Kellam et
al., 1991; McGee, Williams, & Silva, 1985; Rebok, Hawkins, Krener, Mayer, & Kellam, 1996)
and lead to poor academic achievement (Kellam et al., 1991; Rowe & Rowe, 1992) among both
boys and girls, but to depression only among girls (Kellam et al., 1991). Therefore,
understanding the role of gender on attentional performance may help us (1) differentiate normal
gender differences from variations in the manifestation of disorders characterized by impaired
attention and (2) clarify the reasons for the higher vulnerability of boys to these disorders.
We view attention as a multidimensional construct and selected our measures to assess
conceptually different aspects of attention based on the research conducted by Mirsky and
colleagues (Mirsky, 1987; Mirsky, Anthony, Duncan, Ahearn, & Kellam, 1991). Distilling the
large amount of data on attention from cognitive psychology. neuropsychology, and
psychopathology, these investigators suggested that various attention measures reflect different
cognitive processes. Based on the results of a factor analysis of a battery of neuropsychological
tests, these authors identified four separate processes or elements including the ability to
focus; the capacity to maintain or sustain a focus over time; the ability to change or shift
attentive focus in a flexible and adaptive manner; and the attention required to encode
information (e.g., recode, recall, and reorder) once initial registration has occurred (Mirsky et
al., 1991). These investigators identified this factor structure initially in a sample of 203 adults
and subsequently replicated the findings using the present sample (Mirsky et al., 1991). For the
current study, we included the measures employed by these investigators, and added other
measures to assess aspects of attention particularly relevant to classroom activity; these include
the ability to resist distracting events and the capacity to control the intensity of attentive effort.
The children in this study came from varied backgrounds and were not chosen on the
basis of having teacher- or parent-identified attention problems, but rather were representative of
the population at large. A multi-stage sampling and assessment strategy was employed in
selecting the present sample from a larger first-stage sample (Kellam & Rebok, 1992). A
first-stage sample is an entire cohort of children that is representative of a defined population.
The sample described here is referred to as a second-stage sample. This sample is smaller yet
representative of the original population, and is suitable for in-depth investigation. This sample is
particularly relevant for the study of attentional capacities because many of the children came
from urban areas associated with a high risk of attention and behavior problems.
These children are participating in a longitudinal study conducted by the Prevention
Research Center of the Johns Hopkins University. The aim of the study is to identify antecedents
of maladaptive behaviors and to evaluate the effects of two classroom-based intervention
strategies. The study examined the effects of two classroom-based preventive interventions
designed to improve academic achievement and reduce maladaptive aggressive and shy
behaviors (Dolan et al., 1993). There were virtually no effects of intervention on attention
performance in the sample of 435 children, and no significant interactions between intervention
status and gender or intelligence. We report here the results of the initial assessment, completed
when the children were enrolled in the second grade. The composition of the sample and the
nature of the assessment procedures give us a unique opportunity to study individual differences
in attentional performance in a large group of children, eliminating possible referral and
selection biases. As indicated previously, our objective was to examine the influence of gender
and intelligence on various aspects of attention and, more specifically, to address the following
questions:
(1) Will an unselected, non clinical sample of boys and girls differ in their attention
capacities?
(2) What is the relationship between gender differences in attentional performance, if any,
and boys’ increased vulnerability to attention disorders?
(3) What is the relationship between intelligence and various attention elements?
Methods
Subjects
The sample consisted of 435 children selected from an original cohort of first graders
who participated in a collaborative study by the Prevention Research Center of the Johns
Hopkins University and the Baltimore City Public Schools. The original cohort came from
diverse socioeconomic backgrounds and included all children entering the first grade of 19
public schools in Baltimore in the Fall of 1985. The participating schools were selected after a
review of East Baltimore neighborhood characteristics with the Baltimore City Department of
Planning. In this review, five urban areas that differed in terms of income level, ethnicity, family
composition, and type of housing were chosen. Urban area 1 was a predominantly Caucasian
area in which married couples with incomes in the low-to middle range resided in
well-maintained row houses. Urban area 2 comprised large public housing projects and was
mostly African American in composition. Urban area 3 was populated by middle-income African
American families who resided in well-maintained row houses. Urban area 4 was an integrated
area, with middle-income married couples living in detached houses. Urban area 5 was a
Caucasian area, comprised of moderate-income married couples who resided in detached or
semidetached homes. Within each urban area, three or four similar schools matched for
achievement scores, ethnicity, and socioeconomic status were identified.
In the Spring of 1987, 743 children from the original sample (most in second grade) had
parental consent and teacher ratings of classroom behavior and were, therefore, eligible to
participate in the study. Because of time limitations, the entire cohort could not be evaluated. In
order to preserve the representative nature of the population, sampling was carried out with a
replicate sampling scheme, grouping the 19 schools into three replicates of schools matched for
intervention status and urban area. From March to June of 1987, testing was completed for all
children in replicates one and two, and a portion in replicate three. One third of the students in
each of the five schools in replicate three (77 of 230) were selected randomly to remain in the
sampling frame. Within the three replicates, 475 assessments were attempted and 435, or 91.6%,
were completed. Forty children were not assessed because of absences, transfers, and school
scheduling conflicts.
The study sample included 214 boys and 221 girls, most between the ages of 7.5 and 8
years (mean = 7.9 years). The sample was 63.7% African American, 35.3% Caucasian, and also
included 1 Asian American, 3 Native Americans, and 1 Hispanic child. Most children (n = 380)
were enrolled in second grade; 55 children (24 boys and 31 girls), or almost 13% of the sample,
were repeating the first grade. Chi-square comparisons between the study sample and the
remainder of the original cohort did not yield any differences in gender or race between the two
groups.
Measures
Table 1 presents a list of the attention measures included in the study in the order in
which they were administered. This order was chosen to minimize the effects of fatigue and
boredom. As previously indicated, our selection of measures was based on the model developed
by Mirsky and colleagues. In previous publications, some of us found that these measures group
into separate factors and proposed that they assess distinct attentional elements (Mirsky, 1987;
Mirsky et al., 1991). More specifically, the Digit Cancellation task and the Coding subtest of the
WISC-R were found to assess the child's ability to focus on a particular stimulus and make a
rapid response. The Continuous Performance Test (CPT) was considered to be a measure of
sustained attention whereas the Wisconsin Card Sorting Test tapped the capacity to shift the
focus of attention adaptively. Finally, the Arithmetic and Digit Span subtests of the WISC-R
measure the facility to encode information once initial registration had occurred.
In addition to the attention tests, we included the Peabody Picture Vocabulary
Test-Revised (Dunn & Dunn, 1981) as an estimate of verbal intelligence. A description of these
tests follows.
Continuous Performance Tests (CPT)
We administered three versions of the Continuous Performance Test (Rosvold, Mirsky,
Sarason, Bransome, & Beck, 1956) using a Sunrise Systems Yankee II computer and a stimulus
display with a 2 in. x 2 in. screen. In all CPT versions, children saw individual letters presented
on the screen at a rate of one per second, and were instructed to press a response button as
quickly as possible whenever the letter 'X' appeared. For each task, a total of 300 letters was
presented with a 100-ms stimulus duration and a 900-ms interstimulus interval. Targets (Xs)
were presented in a pseudorandom sequence with a probability of .20. Each CPT version
included 60 randomly presented targets and lasted approximately 6 min.
Name of Instrument
Continuous Performance Test: Standard version
Digit Cancellation: Standard condition
Digit Cancellation: Distraction condition
Continuous Performance Test: Auditory distraction version
Wechsler Intelligence Scale for Children-Revised (WISC-R)
Digit span subtest
Coding subtest
Arithmetic subtest
Continuous Performance Test: Degraded version
Wisconsin Card Sorting Test (WCST)
Table 1. Attention Battery.
The three CPT versions varied in terms of stimulus presentation and processing demands.
In the Standard version, the children were instructed to press the response button whenever they
saw an 'X' on the screen. The Degraded CPT was similar to the Standard version except that
the letters were blurred by placing a piece of ground glass over the display screen. In the
Distraction task, children heard a different sequence of letters delivered over earphones at the
same rate as the visually presented letters. They were instructed to ignore the aurally presented
letters and to respond to the visual stimuli. All children received practice trials in each task
prior to beginning testing.
In each version, we recorded the total number of correct responses (hits), omission
errors (misses). commission errors (false alarms), and reaction time for correct responses. This
information was available for the first half (i.e., initial 30 targets) and second half (i.e., second
sequence of 30 targets) of each CPT.
Digit Cancel lotion Task
The Digit Cancellation task was modelled after the distractibility test described by
Lifshitz, Kugelmass, and Karov (1985). Two conditions were included in the present study. In
the Standard condition, children were shown an 8 1/2 in. x 11 in. sheet of paper with a 15 cm x
10 cm array of numbers and were instructed to scan each row of the array as quickly as
possible from left to right and cross out the numbers 3 and 7. In the Distraction condition,
children heard digits presented through earphones at a rate of 1 per s while they were scanning
the array. They were instructed to cross out the numbers 2 and 4 in the array and to ignore the
auditory digits. Each task yielded three scores: time to complete the task, number of targets
missed (omission errors), and number of incorrect responses (commission errors).
Wechsler Intelligence Scale for Children-Revised (WISC-R) (Wechsler, 1974)
The Arithmetic, Coding, and Digit Span subtests of the WISC-R were included as
additional measures of attention. These subtests constitute the freedom-from-distractibility
factor described by Kaufman (1975, 1979).
Wisconsin Card Sorting Test (WCST) (Heaton, 1981)
The WCST is widely used to measure concept formation and the ability to shift set. In
this task, the child is given a set of cards and asked to place each card under a four-choice
display which differ in terms of color, form, or number. The child is not given further
instructions but receives feedback ('right' or 'wrong') after each card placement. After 10
correct responses in one category, the category is changed without informing the subject. In
agreement with standard instructions, the task was discontinued when the child completed six
categories (sorted the cards using each category twice) or had sorted all 128 cards. We also
discontinued the task if a child had not completed any categories after sorting 64 cards. Number
of correct responses, perseverative errors, nonperseverative errors, and failures to maintain set
were used as dependent measures.
Peabody Picture Vocabulary Test-Revised, Form L (PPVT-R) (Dunn & Dunn, 1981)
The PPVT-R is a test of receptive vocabulary that correlates highly with standardized
intelligence tests and was included in this study as an estimate of verbal intelligence. In this task,
the examiner says a word and the child has to point to one picture among a set of four
alternatives that depicts the word.
Procedure
Children were tested individually in their school during regular hours by four examiners
trained in the administration of the instruments. The battery of tests took approximately 70 min
to complete. All children were tested within a 3-month period.
Results
Gender Differences
A series of ANCOVAs controlling for age were performed to compare boys and girls on
the attention measures. Girls outperformed boys on the Digit Cancellation task, where they had
faster completion times and fewer errors of omission (p < .02). Although the same findings were
obtained in the Standard and Distraction conditions, there was a significant interaction between
gender and version of the Digit Cancellation task (p < .001). This interaction accentuated gender
differences in the Distraction condition relative to the Standard condition. In other words,
introducing the distraction slowed the performance of boys more than that of girls and also
resulted in relatively more omission errors (Table 2). Girls also obtained higher scores on the
Coding subtest of the WISC-R. This finding provided further support for the superiority of girls
at focusing attention on a particular stimulus, ignoring irrelevant stimuli, and making a rapid
response.
The CPT results shown in Table 2 also indicated gender differences in response time, in
this case favoring boys. Boys had shorter reaction times to target stimuli in all versions of the
CPT (p < .02) and responded, on the average, 12-18 ms faster than girls. Boys also made more
commission errors in all versions of the CPT, but did not differ from girls in the number of
correct responses (p > .05). Thus, unlike the performance of girls on the Digit Cancellation task,
boys tended to sacrifice accuracy for speed in the CPT
Because boys had faster reaction times and were more likely to respond incorrectly on
the CPT, we examined gender differences in speed-accuracy trade-offs by correlating reaction
time with number of commission errors. These analyses revealed that reaction time was
negatively correlated with commission errors only in the Degraded task (r = -.30 for boys, -.33
for girls; p < .0001). Unexpectedly, the correlations between reaction time and number of errors
in the Standard and Distraction versions were nonsignificant for boys, and positive for girls. That
is, for girls, slower reaction times were associated with a larger number of errors in both the
Standard (r = .27, p < .0001) and Distraction (r = .38, p <.0001) versions.
We then tested possible gender differences in response strategy using signal detection
parameters. We selected the nonparametric indices of sensitivity, α', and response criterion, β',
instead of their parametric counterparts (d' and β) because of the lack of normality in some of our
distributions (Grier, 1971). Our results indicated that boys and girls obtained similar α' values in
all CPTs (p > .10). Differences in β' reached significance only in the Standard version, with boys
obtaining lower scores. Therefore, we obtained only partial support with this metric for the
hypothesis that boys adopt a more lenient response criterion.
There were no other gender differences in CPT performance. Comparisons of the
children's scores in the first and second halves of the CPT did not reveal any significant main
effect of time (i.e., first vs. second half of the task) or significant interactions between time and
gender. Similarly, the Degraded version was more difficult than the Standard and Distraction
version, but there was no significant interaction between task version and gender. These findings
indicate that boys and girls are equally proficient at sustaining a focus of attention over time,
although boys tend to have more difficulty withholding erroneous responses.
Boys and girls did not differ in their capacity to shift their focus of attention, as reflected
by their performance on the WCST (p > .10). They also obtained similar scores on the
Arithmetic and Digit Span subtests of the WISC-R, suggesting a comparable ability to encode
numerical information.
Verbal Intelligence
We examined first the influence of the child's verbal intelligence on attentional
performance through a correlation matrix using age as a covariate. Table 3 displays the values of
these correlations as well as the results of the Fisher Z transformation used to compute
differences in the magnitude of the correlations between boys and girls. Before reviewing the
specific findings, we would like to emphasize that, although the magnitudes of some the
significant correlations are small on an absolute level, these values are of a substantial size for
epidemiological samples.
As can be seen in Table 3, higher verbal intelligence was associated with better attentional
performance for both boys and girls, although this association was generally stronger for boys.
For example, number of errors in the CPT was negatively correlated with PPVT-R scores for
both genders, but the correlations were consistently significant only for boys. For boys,
performance on the PPVT-R was also negatively correlated with completion time on the
Distraction condition of the Digit Cancellation test. In fact, all measures that assess the child's
ability to process information rapidly (e.g., CPT reaction time, completion time in the Digit
Cancellation tasks, and scores in the Coding subtest) tended to be negatively correlated with
PPVT-R
PPVT-R Standard Scores
Girls
Boys
CPT-Standard
Omission errors
Commission errors
Reaction time
-.11
-.14
.01
-.24b,c
-.18a
-.20a,c
CPT-Distraction
Omission errors
Commission errors
Reaction time
-.15
-.26b
.07
-.20a
-.23ba
-.19
CPT-Degraded
Omission errors
Commission errors
Reaction Time
-.18a
-.20a
.02
-.31b,c
-.26b
-.07
Digit Cancellation - Standard
Omission errors
Commission errors
-.20a
-.15
-.13
.04
-.01
-.13
Digit Cancellation - Distraction
Omission errors
Commission errors
Completion time
-.20a
.10
.03
-.15
-.10
-.27b,c
WISC-R (raw scores)
Arithmetic subtest*
Coding subtest*
Digit Span subtest*
.36b
-.09
.18a
.34b
.28b,c
.26b
WCST
Categories*
Perseverative Errors
.31 b
-.16
.26b
-.10
Completion time
* Positive correlations for these measures have the same meaning as negative correlations for the rest of the
measures.
a
p < .01;
b
p < .001;
c
Significant gender differences (p < .05) using the Fisher Z transformation.
Table 3. Age-Corrected Correlations Between PPVT-R Standard Scores and Attention Scores.
scores only for boys. There were no significant associations between these measures and
performance on the PPVT-R for girls. For both boys and girls, PPVT-R scores correlated
significantly with number of categories in the WCST and scores on the Arithmetic and Digit
Span subtests of the WISC-R.
Taken together, these findings indicate that the child’s ability to detect infrequent targets
and inhibit erroneous responses, as measured by the CPT, tends to improve with increasing verbal
intelligence for both boys and girls. An improvement in the ability to encode numerical information
and shift attentive focus is also observed with increasing verbal skills in both genders. In contrast,
the association between intelligence and speed of processing differs between boys and girls.
In order to determine if the relationship between attention and intelligence is consistent
across intelligence levels, we compared the attentional scores of children who differed in their
PPVT-R scores. The mean of PPVT-R standard scores in the present sample was almost one
standard deviation below the standardization sample (87.2 vs. 100 with standard deviations of
16.8 and 15, respectively), with girls obtaining lower scores than boys (85.3 vs. 89.0, p < .05).
The poorer performance of our sample in the PPVT-R is not surprising given that most of the
children came from minority backgrounds, and minority children often obtain lower scores on
this test (Alant & Beukes, 1986; Vaughn-Cooke, 1983). Despite this difference in performance,
PPVT-R standard scores conformed to a normal distribution for both boys and girls. Therefore,
we divided the sample into four groups, with children in each group scoring within one standard
deviation. The four groups consisted of: (1) children with PPVT-R standard scores below 70 (31
boys and 38 girls); (2) children with standard scores between 70 and 84 (59 boys and 75 girls);
(3) children with standard scores between 85 and 99 (67 boys and 65 girls); and (4) children with
PPVT-R scores of 100 or above (57 boys and 43 girls).
As expected given the findings of the correlation analyses, children in the four PPVT-R
groups differed in their attention scores, but the results varied depending on the specific attention
test. For example, the children's scores on the Coding subtest increased steadily from 8.6 in
group 1 to 12.1 in group 4. Similar findings were obtained on the Arithmetic (7.5, 8.2, 9.7, and
11, respectively in each group) and Digit Span (8.4, 9.0, 9.3, and 10.4, respectively) subtests of
the WISC-R. These findings were not surprising given the strong correlation between the
PPVT-R and the WISC-R.
Scores on the WCST also differed according to group membership, although only
children with above average PPVT-R scores (Standard Score > 100; group 4) differed
significantly from the other groups. Post hoc comparisons
revealed that these children completed more categories than their peers (3.8 in group 4 vs. 2.7,
2.7, and 2.9 in groups 1, 2, and 3, respectively) and made more correct responses and fewer
perseverative errors. They did not differ from the other groups in the number of nonperseverative
errors or failures to maintain set (p > .10).
A different pattern of results emerged from analyses of the CPT data. Even though there
was a significant main effect of PPVT-R group in all CPT measures, only children with deficient
verbal capacities differed significantly from their peers. Children with PPVT-R scores below 70
made more commission errors than children with PPVT-R scores above 85 (groups 3 and 4) in
all CPT versions; they also tended to make more omission errors, but this finding only reached
statistical significance for the Degraded version. In fact, the difficulty of children with deficient
verbal skills was always more pronounced in the Degraded CPT. The interaction between
PPVT-R group and version of the Digit Cancellation task provided further support for the view
that these children have more difficulty on tasks that have a higher processing requirement.
Compared to children of average verbal skills, children with deficient verbal capacities (PPVT-R
< 70) made more omission errors and had slower completion times in the Distraction condition
of the Digit Cancellation task than in the Standard condition.
We failed to find any significant interactions between PPVT-R group and time on task on
the CPT (first vs. second half of the task), suggesting that children of various intelligence levels
did not differ in their ability to sustain their focus of attention over time.
Gender and Verbal Intelligence
Reaction time on the CPT showed a significant Gender x Verbal Intelligence interaction.
For boys, reaction time improved steadily with increasing PPVT-R scores; a similar
improvement was not observed in girls. There was also a significant interaction between gender
and PPVT-R group for completion time on the Digit Cancellation tasks (p < .0001). Consistent
with the reaction time data, completion time on the Digit Cancellation task decreased with
increasing
PPVT-R scores in boys, but remained stable in girls. These findings are compatible with the lack
of significant correlations between measures of speed and PPVT-R scores in girls.
Discussion
Previous studies have shown that attention comprises distinct cognitive processes and that
various measures of attention are not interchangeable, but assess these processes differentially in
children as well as in adults (Mirsky, 1987; Mirsky et al., 1991; Pogge, Stokes, & Harvey, 1992;
Wolf & Halperin, 1987). In this study we sought to determine whether the child's gender and
verbal intelligence have a distinct impact on these processes as measured neuropsychologically.
The Role of Gender
Gender differences in attentional performance generally favored girls. Girls had faster
completion times and made fewer errors on the Digit Cancellation task. They also obtained
higher scores on the Coding subtest of the WISC-R and made fewer commission errors on the
CPT. These findings suggest that girls were more skillful at focusing their attention on a
particular target, ignoring extraneous information, and executing a rapid response. Boys, in turn,
had faster reaction times on the CPT and in some cases tended to adopt a more lenient response
strategy.
Some of these gender differences, particularly those that reflect impulsivity or
disinhibition (e.g., higher error rate, faster reaction times, and more lenient response criterion on
the CPT), may reflect differences in maturation rate. Girls mature earlier than boys (Tanner,
1962), and variations in physical maturation are likely to be accompanied by changes in behavior
and cognitive performance. Gender differences in activity level and impulsivity, for example, are
in part mediated by maturation status (Eaton & Piklai-Yu, 1989; Maccoby & Jacklin, 1974).
Maturity may also be playing a role in gender differences in cognitive impulsivity. Reports that
younger children are faster and make more commission errors on the CPT than older children
(Greenberg & Waldman, 1993) are consistent with this hypothesis. Further evidence for the
maturation hypothesis is provided by the results of a study by Gale and Lynn (1972). These
investigators found that girls made fewer errors on a vigilance task at 7, 8, and 12 years of age,
but did not differ from boys between 9 and 11 years. The particular ages at which girls performed
better than boys coincide with 'spurts' in brain development (Epstein, 1974; Hudspeth & Pribram,
1992) and, therefore, the girls' advantage at these ages may be secondary to their being further
along the developmental path.
Other gender differences in attentional performance are likely to be more enduring and to
reflect differences in brain organization. The brain is subject to the organizational effects of
gonadal steroid hormones during sensitive periods in gestation or early postnatal life, and these
effects influence gender dimorphic behaviors as well as cognitive performance. There are
well-established differences between males and females in cognitive abilities. Males typically
perform better on mathematical reasoning tasks and on tasks that require complex visual-spatial
skills, whereas females excel on tests of verbal fluency, manual dexterity, and visual scanning
(Gouchie & Kimura, 1991; Kimura, 1992). Although gender differences in rearing practices and
experience may contribute to variations in cognitive performance, the role of hormones appears
to be substantial. For example, gender differences in cognitive abilities, particularly in
visual-spatial skills, become more pronounced at puberty (Petersen, 1976), suggesting that
hormonal changes play a role in their ontogenesis. Further, these differences can be reversed or
attenuated in conditions characterized by aberrant levels of gender hormones such as Turner
Syndrome (Murphy et al., 1994) and adrenal hyperplasia (Hines & Shipley, 1984; Resnick,
Berenbaum, Gottesman, & Bouchard, 1986), and are affected by fluctuations in hormonal levels
during the menstrual cycle (Becker, Creutzfeldt, Schwibbe, & Wuttke, 1982; Jensen, 1982).
Gender differences in perceptual speed and visual scanning may account for the girls'
excellence in some of the attention tests, most notably, in the Coding subtest and the Digit
Cancellation task. Both the Digit Cancellation task and the CPT require the subject to focus on
relevant stimuli while simultaneously inhibiting irrelevant responses (Cooley & Morris, 1990).
We know from the CPT results that boys and girls do not differ in their ability to focus and
detect infrequent targets, even in the more demanding versions of this task. Our findings also
indicate that they were equally proficient at ignoring distracting auditory information and at
sustaining a focus of attention over time. Yet, these tasks differ in important ways. On the CPT,
for example, the stimuli are presented individually, whereas on the Digit Cancellation task and
the Coding subtest, the child is required to scan a visual array rapidly and make a response.
Therefore, it is possible that girls' superiority on these tasks is secondary to their superior
perceptual speed and visual scanning abilities.
The Role of Intelligence
Our findings indicate that verbal intelligence has an impact on attention, but that this
effect is not consistent across levels of performance or attention processes. For example, the
child's ability to encode information, as measured by the Arithmetic and Digit Span subtests of
the WISC-R, improves steadily with increasing PPVT-R scores. A s i mi l a r trend was not
observed in the capacity to shift attentional focus, as assessed by the WCST. On this test, only
children with above average verbal skills (PPVT-R scores > 100) showed a beneficial effect of
these skills in their performance on this task. Performance on the WCST is mediated by parts of
the frontal cortex (Milner, 1963; Robinson, Heaton, Lehman, & Stilson, 1980) which are not
fully matured until late adolescence or early adulthood (Hudspeth & Pribram, 1992; Yakovlev &
LeCours, 1967). The importance of maturation may be especially pronounced in this age group
because these children may vary considerably in the functional maturity of their frontal cortices,
and only a limited number of children may possess the maturity or skills necessary to perform
adequately on this task.
In contrast, we found that children with verbal intelligence ranging from the low average
to the very superior range did not differ significantly in their ability to sustain their focus of
attention and inhibit impulsive responses on the CPT. In fact, only children with deficient verbal
intelligence (i.e., PPVT-R scores < 70) performed worse than their peers. This finding sheds
some light into the lack of association between intelligence and performance on vigilance tasks in
children with average cognitive capacities (Halperin et al., 1991) and the lower performance of
children with mental retardation on these tasks (Warm & Berch, 1985).
In addition, we did not find a strong association between intelligence level and ability to
ignore extraneous information. Children with low verbal intelligence were not found to be more
distractible or susceptible to irrelevant information. Similarly, these children did not differ in
their ability to sustain their focus of attention over time. These results do not support Stankov's
(1989) hypothesis that individuals with higher IQs would show a stronger vigilance decrement
because they lose interest in monotonous tasks more easily than individuals with low IQs. These
findings are also inconsistent with the results reported by Hagen and Huntsman (1971). These
investigators found that children with mental retardation had more difficulty attending to
relevant stimuli and ignoring irrelevant information than children with average intelligence.
However, in Hagen and Huntsman's study only institutionalized children with mental retardation
differed significantly from controls, whereas children with mental retardation residing at home
(who are most likely similar to the present group) did not.
Consistent with Carter and Swanson's (1995) interpretation, our results indicate that
children with poor verbal skills suggestive of mental retardation have limitations in their
information processing capacities beyond their attention problems. Stated in another way,
children with limited cognitive skills do not appear impaired visà-vis normal children on tasks
requiring sustained or focused attention so long as the processing requirements of the tasks are
not excessive. This notion was supported by the relative impairment these children exhibited on
the Degraded CPT and on the Distraction version of the Digit Cancellation task. Therefore, tasks
with greater processing demands (Schneider & Shiffrin, 1977; Shiffrin & Schneider, 1977)
affected these c hi l dr e n differentially, and appeared to be more sensitive to the effects of
cognitive impairment.
The Interaction of Gender and Intelligence
Given that boys, as a group, are more active and impulsive than girls, some investigators
have suggested that these behaviors may interfere with their cognitive performance (Maccoby,
1967; Maccoby & Jacklin, 1974). In other words, impulsivity and overactivity are negatively
associated with intelligence only for boys. Our findings, however, did not support this
hypothesis. For example, although the correlation between verbal intelligence and number of
commission errors in the CPT, commonly thought to reflect impulsivity, was stronger for boys,
both genderes showed the same pattern of performance. Furthermore, measures of speed showed
the opposite relationship. Speed of processing was not correlated with verbal intelligence for
girls, but was positively correlated for boys. These findings suggest that cognitive impulsivity is
distinguishable from rate of responding, and that only impulsivity is associated with lower
cognitive performance.
Implications for Clinical Practice
The present results emphasize the impact of gender and intelligence on attentional
performance, and may have important implications for clinical practice.
Although children with mental retardation tend to do worse on attention tests than
children with normal IQs (e.g., Hagen & Huntsman; 1971; Rosvold et al., 1956; Warm & Berch,
1985), our results suggest that these children may have particular difficulties only on tasks with
high processing demands. As a result, simple attention tasks can be developed to diagnose
specific attention deficits in this population. The importance of devising appropriate assessment
procedures to identify attention deficits in children with mental retardation is emphasized by the
positive response that these children exhibit to a variety of psychopharmacological
intervent i o n s (Dickerson-Mayes, Crites, Bixler, Humphrey, & Mattison, 1994; Payton,
Burkhart, Hersen, & Helsel, 1988).
Our results also suggest that using the same norms to identify boys and girls with
attention problems would likely result in a greater number of false negative diagnoses among girls,
and that the lower prevalence of attention disorders in girls may be an artifact of our diagnostic
procedures. Several investigators have proposed that girls may not be identified as having
AD/HD as often as boys because they exhibit fewer behavioral problems (Berry et al., 1985;
Brown et al., 1991). The present results suggest, further, that girls may not be identified as often
as boys because they perform better on objective measures of attention. Studies of children
referred for AD/HD either report no gender differences in attention (Breen, 1989; Brown et al.,
1991; Horn, Wagner, & Ialongo, 1989) or find more cognitive and learning problems in the girls
(Berry et al., 1985; James & Taylor, 1990). Given that girls generally perform better on attention
tests, it is possible that we are diagnosing with AD/HD only those girls with severe difficulties,
and that, moreover, more girls may benefit from treatment than are currently being treated.
One could argue that by identifying girls that are impaired in attentional performance
relative to their same-gender peers, we are in fact selecting a group of girls that do not have any
significant problems. However, our analyses do not support this notion. We have found that girls
who obtain lower scores on the attention tests also perform poorer academically, have lower
verbal intelligence, and are rated by their teachers as having more maladaptive behaviors than
their same-gender peers (Anthony et al., 1997).
Relation of Attention to Brain Organization and Development
In prior publications, some of us (Mirsky, 1987; Mirsky et al., 1991) have speculated that
the attention system in the brain is a complex system, paralleling the complexity of the
behavioral functions encompassed within the rubric of "attention". According to this
formulation, different attentional functions are supported by distinct cerebral regions, which are
nevertheless integrated into a single, functional entity. For example, neural elements in the
thalamic and upper brainstem portions of the attention system support the vigilance or sustain
attention function tapped by the CPT. Similarly, arithmetic or digit span recall, identified as the
encoding function of attention is mediated by limbic structures such as the amygdala and
hippocampus, and the capacity-to-shift the focus of attention depends on the integrity of parts of
the dorsolateral prefrontal cortex.
Some of the brain regions involved in the support of attentional functions are not fully
myelinated until adolescence (Hudspeth & Pribram, 1992; Yakovlev & LeCours, 1967). Prior
research has shown that apparently identical cerebral lesions in immature animals and adults of
the same species may have different effects on behavior, depending on whether the damaged
structure has achieved adult form at the time of the lesion. Thus, Goldman (1971, 1974) reported
that the effects of certain lesions in young animals were not detectable until the behavior
dependent upon the structure in question would normally have been expected to appear. This
formulation is also supported by the work of Epstein, who noted that, 'there is no catch-up in
intellectual level, and the building of an adolescent brain on top of an architecturally abnormal
brain yields an even greater abnormality' (Epstein, 1974, pp. 343-370). In the present context we
note that the published reports of differences between children and adults in the effects of head
trauma on attention (with less severe effects seen in children than in adults) may relate to the
immaturity of certain regions of the child's brain at the time of the insult.
The implications of these studies for the present investigation are as follows: There may
have been compromises to the brain of some of these children which may not be manifest until
they reach adolescence. We have continued to assess this cohort of children into the middle
school years in order to study the developmental trajectory of the several attentional processes
and the continued impact of gender and intelligence on attention performance. These
assessments will be the subject of subsequent reports.
Acknowledgements
We acknowledge the contributions of the City of Baltimore, its families and children, and the
administration, faculty, and staff of the Baltimore City Public Schools. In particular we would like to thank Dr.
Walter Amprey, Superintendent of Baltimore City Public Schools; Dr. Lillian Gonzales and Dr. Patsy
Blacksheare, Deputy Superintendents; Dr. Juanita Lewis, Deputy Director; and the following Administrators who
were, then, in the positions noted; Dr. Denise Borders, Chief of Accountability; Robert Solomon, Director, Special
Education; Dr. Carla Ford, Specialist, Early Childhood Education; Louise Fink, Coordinator, Social Work Services;
Dale Parker-Brown, Director, Compensatory Education; Matthew Riley, Director of the Eastern District; and Willie
Foster, Director of Middle Schools. The studies on which this paper is based have been supported by the following
grants, with supplements from the National Institute on Drug Abuse: National Institute of Mental Health (NIMH)
Grant Number P50 MH38725, Epidemiologic Prevention Center for Early Risk Behavior; NIMH Grant Number
1R01 MH42968, Periodic Outcome of Two Preventive Trials; NIMH Grant Number 1R01 MH40859, Statistical
Methods for Mental Health Preventive Trials.
Address correspondence to: Dr. Daisy Pascualvaca, Section on Clinical and Experimental
Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Building 15K, Room
103, Bethesda, Md 20892, USA.
Accepted for publication: November 1, 1996.
References
Alant, E., & Beukes, S. M. (1986). The application of the Peabody Picture Vocabulary Test-Revised (PPVT-R) to
non-mainstream children. South African Journal of Communication Disorders, 33, 7-14.
American Psychiatric Association (1994). Diagnostic and statistical manual of mental disorders (4th ed.).
Washington, DC: Author.
Anthony, B. J., Rebok, G. W., Pascualvaca, D. M., Jensen, P., Ahearn, M. B., Kellam, S. G., & Mirsky, A. F.
(1997). Epidemiological investigation of attention performance in children. Relationships to classroom behavior
and academic performance. Manuscript submitted for publication.
Becker, D., Creutzfeldt, O. D., Schwibbe, M., & Wuttke, W. (1982). Changes in physiological, EEG and
psychological parameters in women during the spontaneous menstrual cycle and following oral contraceptives.
Psychoneuroendocrinology, 7, 75-90.
Berry, C. A., Shaywitz, S. E., & Shaywitz, B. A. (1985). Girls with attention deficit disorder: A silent minority? A
report on behavioral and cognitive characteristics. Pediatrics, 76, 801-809.
Breen, M. J. (1989). Cognitive and behavioral differences in ADHD boys and girls. Journal of Child Psychology,
Psychiatry, and Allied Disciplines, 30, 711-716.
Brown, R. T., Madan-Swain, A., & Baldwin, K. (1991). Gender differences in a clinic-referred sample of
attention-deficit-disordered children. Child Psychiatry and Human Development, 22, 111-128.
Cantwell, D. P. (1975). Genetics and hyperactivity. Journal of Child Psychology and Psychiatry and Allied
Disciplines, 16, 261-264.
Carter, J. D., & Swanson, H. L. (1995). The relationship between intelligence and vigilance in children at risk.
Journal of Abnormal Child Psychology, 23, 201-220.
Chelune, G. J., & Baer, R. A. (1986). Developmental norms for the Wisconsin Card Sorting Test. Journal of
Clinical and Experimental Neuropsychology, 8, 219-228.
Cooley, E. L., & Morris, R. D. (1990). Attention in children: A neuropsychological based model for assessment.
Developmental Neuropsychology, 6, 239-274.
Dickerson-Mayes, S., Crites, D. L., Bixler, E. O., Humphrey, F. J., & Mattison, R. E. (1994). Methylphenidate and
ADHD: Influence of age, IQ, and neurodevelopmental status. Developmental Medicine and Child Neurology, 36,
1099-1107.
Dolan, L., Kellam, S. G., Brown, C. H., Wertharmer-Larsson, L., Rebok, G., Mayer, L., Laudolff, J., Turkkan, J. S.,
Ford, C., & Wheeler, L. (1993). The short-term impact of two classroom-based preventive interventions on
aggressive and shy behaviors and poor achievement. Journal of Applied Developmental Psychology, 14,
317-345.
Dunn, L. M., & Dunn, L. M. (1981). Peabody Picture Vocabulary Test-Revised. Circle Pines, MN: American
Guidance Service.
Eaton, W. O., & Piklai-Yu, A. (1989). Are gender differences in child motor activity level a function of gender
differences in maturational status? Child Development, 60, 1005-1011.
Epstein, H. T. (1974). Phrenoblysis: Special brain and neural growth periods: Human mental development.
Developmental Psychobiology, 7, 217-224.
Gale, A., & Lynn, R. (1972). A developmental study of attention. British Journal of Educational Psychology, 42,
260-266.
Goldman, P. S. (1971). Functional development of the prefrontal cortex in early life and the problem of neuronal
plasticity. Experimental Neurology, 32, 366-387.
Goldman, P. S. (1974). Recovery of function after CNS lesions in infant monkeys. Neuroscience Research Progress
Bulletin, 12, 217-222.
Gouchie, C., & Kimura, D. (1991). The relationship between testosterone levels and cognitive ability patterns.
Psychoneuroendocrinology, 16, 323-334.
Greenberg, L. M., & Waldman, I. D. (1993). Developmental normative data on the test of variables of attention
(T.O.V.A.). Journal of Child Psychology, Psychiatry and Allied Disciplines, 34, 19-30.
Grier, J. B. (1971). Nonparametric indexes for sensitivity and bias. Psychological Bulletin, 75, 424-429.
Hagen, J. W., & Huntsman, N. J. (1971). Selective attention in mental retardates. Developmental Psychology, 5,
151-160.
Halperin, J. M., Sharma, V., Greenblatt, E., & Schwartz, S. T. (1991). Assessment of the continuous performance
test: Reliability and validity in a nonreferred sample. Psychological Assessment: A Journal of Consulting and
Clinical Psychology, 3, 603-608.
Halperin, J. M., Wolf, L. E., Pascualvaca, D. M., Newcorn, J. H., Healey, J. M., O'Brien, J. D., Morganstein, A., &
Young, J. G. (1988). Differential assessment of attention and impulsivity in children. Journal of the American
Academy of Child and Adolescent Psychiatry, 27, 326-329.
Heaton, R. K. (1981). The Wisconsin Card Sorting Test. Odessa: Psychological Assessment Resources.
Hines, M., & Shipley, C. (1984). Prenatal exposure to Diethylstilbestrol (DES) and the development of
genderual dimorphic cognitive abilities and cerebral lateralization. Developmental Psychology, 20, 81-94.
Horn, W. F., Wagner, A. E., & Ialongo, N. (1989). Gender differences in school-aged children with pervasive
attention deficit hyperactivity disorder. Journal of Abnormal Child Psychology, 17, 109-125.
Hudspeth, W. J., & Pribram, K. H. (1992). Psychophysiological indices of cerebral maturation. International
Journal of Psychophysiology, 12, 19-29.
James, A., & Taylor, E. (1990). Gender differences in the hyperkinetic syndrome of childhood. Journal of Child
Psychology, Psychiatry, and Allied Disciplines, 34, 437-446.
Jensen, B. K. (1982). Menstrual cycle effects on task performance examined in the context of stress research. Acta
Psychologica, 50, 159-178.
Kaufman, A. S. (1975). Factor analysis of the WISC-R at 11 age levels between 6 1/2 and 16 1/2 years. Journal of
Consulting and Clinical Psychology, 43, 135-147.
Kaufman, A. S. (1979). Intelligent testing with the WISC-R, New York: John Wiley & Sons.
Kellam, S.F., & Rebok, G.W. (1992). Building etiological theory through developmental epidemiologically-based
preventive intervention trials. In J. McCord & R.E. Tremblay (Eds.), Preventing antisocial behavior:
Intervention from birth through adolescence (pp. 162-195). New York: Guilford Press.
Kellam, S. G., Branch, J. D., Agrawal, K. C., & Ensminger, M. E. (1975). Menial health and going to school: The
Woodlawn program of assessment, early intervention and evaluation. Chicago: University of Chicago Press.
Kellam, S. G., Werthamer-Larsson, L., Dolan, L., Brown, C. H., Mayer, L., Rebok, G. W., Anthony, J. C., Laudolff,
J., Edelsohn, G., & Wheeler, L. (1991). Developmental epidemiologically based preventive trials: Baseline
modeling of early target behaviors and depressive symptoms. American Journal of Community Psychology, 19,
563-584.
Kimura, D. (1992). Cognitive function: Gender differences and hormonal influences. Neuroscience, S2, 41 - 43.
Kirchner, G. L., & Knopf, I. J. (1974). Differences in vigilance performance of second-grade children as related to
gender and achievement. Child Development, 45, 490-495.
Levy, F. (1980). The development of sustained attention (vigilance) and inhibition in children: Some normative
data. Journal of Child Psychology, Psychiatry, and Allied Disciplines, 21, 77-84.
Levy, F., & Hobbes, G. (1979). The influences of social class and gender on sustained attention (vigilance) and
motor inhibition in children. Australian and New Zealand Journal of Psychiatry, 13, 231-234.
Lifshitz, M., Kugelmass, S., & Karov, M. (1985). Perceptual-motor and memory performance of high-risk children.
Schizophrenia Bulletin, 11, 74-84.
Maccoby, E. E. (1967). The development of gender differences. London: Tavistock.
Maccoby, E. E., & Jacklin, C. N. (1974). The psychology of gender differences. Stanford, CA: Stanford University
Press.
McGee, R., Williams, S., & Silva, P. A. (1985). Factor structure and correlates of ratings of inattention,
hyperactivity, and antisocial behavior in a large sample of 9 year-old children from the general population.
Journal of Consulting and Clinical Psychology, 53, 480-490.
Milner, B. (1963). Effects of different brain lesions on card sorting. Archives of Neurology, 9, 90-100.
Mirsky, A. F. (1987). Behavioral and psychophysiological markers of disordered attention. Environmental Health
Perspectives, 74, 191-199.
Mirsky, A. F., Anthony, B. J., Duncan, C. C., Ahearn, M. B., & Kellam, S. G. (1991). Analysis of the elements of
a t t e n t io n : A neuropsychological approach. Neuropsychology Review, 2, 109-145.
Murphy, D. G. M., Allen, G., Haxby, J. V., Largay, K. A., Daly, E., White, B. J., Powell, C. M., & Schapiro, M. B.
(1994). The effects of gender steroids, and the X chromosome, on female brain function: A study of the
neuropsychology of adult Turner Syndrome. Neuropsychologia, 32, 1309-1323.
Offord, D. R., Boyle, M. H., Szatmari, P., Rae-Grant, N., Links, P. S., Cadman, D. T., Byles, J. A., Crawford, J. W.,
Munroe Blum, H., Byrne, C., Thomas, H., & Woodward, C. (1987). Ontario child health study: Six-month
prevalence of disorder and rates of service utilization. Archives of General Psychiatry, 44, 832-836.
Payton, J. B., Burkhart, J. E., Hersen, M., & Helsel, W. J. (1988). Treatment of ADHD in mentally retarded
children: A preliminary study. Journal of the American Academy of Child and Adolescent Psychiatry, 28,
761-767.
Petersen, A. C. (1976). Physiological androgyny and cognitive function in adolescence. Developmental Psychology,
12, 524-533.
Pogge, O. L., Stokes, J., & Harvey, P. D. (1992). Empirical evaluation of the neuropsychology of attention: A
confirmatory factor analysis of the Mirsky model in adolescents inpatients. Biological Psychiatry, 31, 112A.
Rebok, G. W., Hawkins, W. E., Krener, P., Mayer, L. S., & Kellam, S. G. (1996). Effect of concentration problems
on the malleability of children's aggressive and shy behaviors. Journal of the American Academy of Child and
Adolescent Psychiatry, 35, 193-203.
Resnick, S. M., Berenbaum, S. A., Gottesman, I.I., & Bouchard, T. J. (1986). Early hormonal influences on
cognitive functioning in Congenital Adrenal Hyperplasia. Developmental Psychology, 22, 191-198.
Robinson, A. L., Heaton, R. K., Lehman, R. A. W., & Stilson, D. W. (1980). The utility of the Wisconsin Card
Sorting Test in detecting and localizing frontal lobe lesions. Journal of Consulting and Clinical Psychology, 48,
605-614.
Rosvold, H. E., Mirsky, A. F., Sarason, I., Bransome, E. Jr., & Beck, L. H. (1956). A continuous performance test
of brain damage. Journal of Consulting Psychology, 20, 343-350.
Rowe, K. J., & Rowe, K. S. (1992). The relationship between inattentiveness in the classroom and reading
achievement (Part B): An explanatory study. Journal of the American Academy of Child and Adolescent
Psychiatry, 31, 357-368.
Rutter, M., Tizard, J., & Whitmore, K. (1970). Education, health and behavior. London: Longman.
Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic information processing: I. Detection, search
and attention. Psychological Review, 81, 1-66.
Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual
learning, automatic attending, and a general theory. Psychological Review, 87, 127-190.
Stankov, L. (1989). Attentional resources and intelligence: A disappearing link. Personality and Individual
Differences, 10, 957-968.
Tanner, J. M. (1962). Growth at adolescence. Oxford: Blackwell Scientific Publications.
Vaughn-Cooke, B. F. (1983). Improving language assessment in minority children. American Speech and Hearing
Association, September, 29-33.
Warm, J. S., & Berch, D. B. (1985). Sustained attention in the mentally retarded: International Review of Research
in Mental Retardation, 13, 28-33.
Weber, A. M., & Segalowitz., S. J. (1990). A measure of children's attentional capacity. Developmental
Neuropsychology, 6, 13-23.
Wechsler, D. (1974). Wechsler Intelligence Scale for Children-Revised. New York: Psychological Corporation.
Wolf, L. E., & Halperin, J. M. (1987). Assessment of attention in psychiatrically disturbed children: What are we
really measuring? Journal of Clinical and Experimental Neuropsychology, 9, 61.
Yakovlev, P. I., & LeCours, A. R. (1967). The mylogenetic cycles of regional maturation of the brain. In A.
Minkowski (Ed.), Regional development of the brain in early life (pp. 3-70). Oxford, England: Blackwell.